A hybrid approach based on genetic algorithms to solve the problem of cutting structural beams in a metalwork company

This work presents a hybrid approach based on the use of genetic algorithms to solve efficiently the problem of cutting structural beams arising in a local metalwork company. The problem belongs to the class of one-dimensional multiple stock sizes cutting stock problem, namely 1-dimensional multiple stock sizes cutting stock problem. The proposed approach handles overproduction and underproduction of beams and embodies the reusability of remnants in the optimization process. Along with genetic algorithms, the approach incorporates other novel refinement algorithms that are based on different search and clustering strategies.Moreover, a new encoding with a variable number of genes is developed for cutting patterns in order to make possible the application of genetic operators. The approach is experimentally tested on a set of instances similar to those of the local metalwork company. In particular, comparative results show that the proposed approach substantially improves the performance of previous heuristics.Gracia Calandin, CP.; Andrés Romano, C.; Gracia Calandin, LI. (2013). A hybrid approach based on genetic algorithms to solve the problem of cutting structural beams in a metalwork company. Journal of Heuristics. 19(2):253-273. doi:10.1007/s10732-011-9187-xS253273192Aktin, T., Özdemir, R.G.: An integrated approach to the one dimensional cutting stock problem in coronary stent manufacturing. Eur. J. Oper. Res. 196, 737–743 (2009)Alves, C., Valério de Carvalho, J.M.: A stabilized branch-and-price-and-cut algorithm for the multiple length cutting stock problem. Comput. Oper. Res. 35, 1315–1328 (2008)Anand, S., McCord, C., Sharma, R., et al.: An integrated machine vision based system for solving the nonconvex cutting stock problem using genetic algorithms. J. Manuf. 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Res. 9, 849–859 (1961)Gilmore, P.C., Gomory, R.E.: A linear programming approach to the cutting stock problem. Part II. Oper. Res. 11, 863–888 (1963)Ghiani, G., Laganà, G., Laporte, G., Mari, F.: Ant colony optimization for the arc routing problem with intermediate facilities under capacity and length restrictions. J. Heuristics 16, 211–233 (2010)Gonçalves, J.F., Resende, G.C.: Biased random-key genetic algorithms for combinatorial optimization. J. Heuristics (2011). doi: 10.1007/s10732-010-9143-1Gradisar, M., Kljajic, M., Resinovic, G., et al.: A sequential heuristic procedure for one-dimensional cutting. Eur. J. Oper. Res. 114, 557–568 (1999)Haessler, R.W.: One-dimensional cutting stock problems and solution procedures. Math. Comput. Model. 16, 1–8 (1992)Haessler, R.W., Sweeney, P.E.: Cutting stock problems and solution procedures. Eur. J. Oper. Res. 54(2), 141–150 (1991)Haessler, R.W.: Solving the two-stage cutting stock problem. 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Ant colony optimisation and local search for bin-packing and cutting stock problems

The Bin Packing Problem and the Cutting Stock Problem are two related classes of NP-hard combinatorial optimization problems. Exact solution methods can only be used for very small instances, so for real-world problems, we have to rely on heuristic methods. In recent years, researchers have started to apply evolutionary approaches to these problems, including Genetic Algorithms and Evolutionary Programming. In the work presented here, we used an ant colony optimization (ACO) approach to solve both Bin Packing and Cutting Stock Problems. We present a pure ACO approach, as well as an ACO approach augmented with a simple but very effective local search algorithm. It is shown that the pure ACO approach can compete with existing evolutionary methods, whereas the hybrid approach can outperform the best-known hybrid evolutionary solution methods for certain problem classes. The hybrid ACO approach is also shown to require different parameter values from the pure ACO approach and to give a more robust performance across different problems with a single set of parameter values. The local search algorithm is also run with random restarts and shown to perform significantly worse than when combined with ACO

Shadow Price Guided Genetic Algorithms

The Genetic Algorithm (GA) is a popular global search algorithm. Although it has been used successfully in many fields, there are still performance challenges that prevent GA’s further success. The performance challenges include: difficult to reach optimal solutions for complex problems and take a very long time to solve difficult problems. This dissertation is to research new ways to improve GA’s performance on solution quality and convergence speed. The main focus is to present the concept of shadow price and propose a two-measurement GA. The new algorithm uses the fitness value to measure solutions and shadow price to evaluate components. New shadow price Guided operators are used to achieve good measurable evolutions. Simulation results have shown that the new shadow price Guided genetic algorithm (SGA) is effective in terms of performance and efficient in terms of speed

Cuckoo Search Approach for Cutting Stock Problem

Cutting Stock Problem has been used in many industries like paper, glass, wood and etc. Cutting Stock Problem has helped industries to reduce trim loss and at the same time meets the customer’s requirement. The purpose of this paper is to develop a new approach which is Cuckoo Search Algorithm in Cutting Stock Problem. Cutting Stock Problem with Linear Programming based method has been improved down the years to the point that it reaches limitation that it cannot achieve a reasonable time in searching for solution. Therefore, many researchers have to turn to metaheuristic algorithms as a solution to the problem which also makes these algorithms become famous. Cuckoo Search Algorithm is selected because it is a new algorithm and outperforms many algorithms. Hence, this paper intends to experiment the performance of Cuckoo Search in Cutting Stock Problem

A Cutting Plan of One-Dimensional Construction Materials to Reduce Loss in Construction Projects.

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Poolability and Aggregation Problems of Regional Innovation Data: An Application to Nanomaterial Patenting

Research and development (R&D) in the field of nanomaterials is expected to be a major driver of innovation and economic growth. In this respect, many countries, as national systems of innovation, have established support programs offering subsidies for industry- and government-funded R&D. Consequently, it is of great interest to understand which factors facilitate the creation of new technological knowledge. The existing literature has typically addressed this question by employing a knowledge production function based on firm-, regional- or even country-level data. Estimating the effects for the entire national system of innovation, however, implicitly assumes poolability of regional data. We apply our reasoning to Germany, which has well-known – and wide – regional disparities, for example between the former East and West. Based on analyses at the level of NUTS-3 regions, we find different knowledge production functions for the East and the West. Moreover, we investigate how our results are affected by the adoption of alternative aggregation levels. Our findings have implications for further research in the field, that is, a careful evaluation of poolability and aggregation is required before estimating knowledge production functions at the regional level. Policy considerations are offered as well.nanotechnology, patents, poolability, aggregation, Germany, spatial autocorrelation, spatial filtering

A multi-objective approach based on soft computing techniques for production scheduling in Corrugator manufacturing plants

AbstractThe corrugator scheduling problem is a difficult problem due to a wide varietyof parameters and optimisation objectives that have to be accounted for andthe relationships among them. Majority of solution techniques proposed so faronly deal with minimizing either, the trim waste or pattern changes, this paperproposes a multi-objective evolutionary algorithm to optimize the WPL objective(weighted planning level) and the cost objectives. Computational experimentswere conducted and results were compared against the current shop schedulingmethod used at a real-life corrugator manufacturing facility. A series of experimentswere also conducted to determine the evolutionary algorithm parameters. Theimprovement on performance metrics encourages us to actually implement thealgorithm at the factory.The corrugator scheduling problem is a difficult problem due to a wide varietyof parameters and optimisation objectives that have to be accounted for andthe relationships among them. Majority of solution techniques proposed so faronly deal with minimizing either, the trim waste or pattern changes, this paperproposes a multi-objective evolutionary algorithm to optimize the WPL objective(weighted planning level) and the cost objectives. Computational experimentswere conducted and results were compared against the current shop schedulingmethod used at a real-life corrugator manufacturing facility. A series of experimentswere also conducted to determine the evolutionary algorithm parameters. Theimprovement on performance metrics encourages us to actually implement thealgorithm at the factory

The German East-West Divide in Knowledge Production: An Application to Nanomaterial Patenting

Research and development (R&D) in the field of nanomaterials is expected to be a major driver of innovation and economic growth. In this respect, many countries, as national systems of innovation, have established support programs offering subsidies for industry- and government-funded R&D. Consequently, it is of great interest to understand which factors facilitate the creation of new technological knowledge. The existing literature has typically addressed this question by employing a knowledge production function based on firm-, regional- or even country-level data. Estimating the effects for the entire national system of innovation, however, implicitly assumes poolability of regional data. We apply our reasoning to Germany, which has well-known – and wide – regional disparities, for example between the East and the West. Based on analyses at the level of NUTS-3 regions, we find different knowledge production functions for the East and the West. Moreover, we investigate how our results are affected by the adoption of alternative aggregation levels. Our findings have implications for further research in the field, that is, a careful evaluation of poolability and aggregation is required before estimating knowledge production functions at the regional level. Policy considerations are offered as well.nanotechnology, patents, poolability, Germany, spatial autocorrelation